ANCOVA and Blocking: 2 Vital Parts to DOE
Previously in our Minitab designed experiment on driving the golf ball as far as possible from the tee, we tested our four experimental factors and determined how many runs we needed to produce a complete data set. Now let’s analyze the data and interpret the covariates and blocking variables.
You Use Minitab. Your New Job Doesn't (YET). What Do You Do?
Rafael's previous employers used Minitab, giving him ample opportunity to figure out how to define experiments and variables to optimize detergent formulas most effectively. But Rafael's new employers didn't use Minitab. His boss challenged him to prove results before considering the investment.
Mulligan? How Many Runs Do You Need to Produce a Complete Data Set?
In our continuing effort to use experimental design to understand how to drive the golf ball the farthest off the tee, we have decided each golfer will perform half the possible combinations of high and low settings for each factor. But how many times should each golfer replicate their runs to produce a complete data set?
Predicting World Cup 2018 with Ordinal Logistic Regression
According to a recent BBC article, England has 4% chance to win the World Cup 2018. I make some predictions using Minitab after gathering data from past World Cup winners. Will this all make a difference? Let’s find out!
5 Reasons Factorial Experiments Are So Successful
Last week we began an experimental design trying to get at how to drive the golf ball the farthest off the tee by characterizing the process and defining the problem. This week we'll design the data collection plan we’ll use to study the factors in the experiment.
A (Golf) Course in Design of Experiments
As we prepare for the inaugural Minitab Insights golf tournament in Scottsdale, Arizona on September 12, we are taking a look back at this series on using Minitab to improve our game. In this first installment, we examine how solving an age-old problem in golf is much like process engineering.
Power and Sample Size – Your Insurance Policy for Statistical Analysis
When we do statistical analyses, like hypothesis testing and design of experiments, we are using a sample of data to answer questions about all of our data. The reliability of these answers is affected by the size of the sample we analyze. To minimize the risk of doing unreliable statistical analysis we can use the Power and Sample size before collecting any data to determine how much data is needed to have a good chance of finding that effect, if it exists. The minimum recommended value for this is 80%.
Sealing Up Patient Safety with Monte Carlo Simulation
If you have a process that isn’t meeting specifications, using the Monte Carlo simulation and optimization tools in Companion by Minitab can help. Here’s how you, as an engineer in the medical device industry, could use Companion to improve a packaging process and help ensure patient safety.
3 Big Lessons from Continuous Improvement Leaders at PEX Europe Spring, London
At Process Excellence (PEX) Europe Spring 2018 there was a lot to learn as Continuous Improvement practitioners and leaders met to 'share and challenge everything you need to be a business of the future.' Here Minitab shares three key CI lessons that will help you to replicate success.
How Can You Fix the Process and Improve Product Development with Simulated Data? See All the Scenarios with Monte Carlo
How do you commit to realistic forecasts and timelines when resources are limited or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo Simulation comes in. Check out this step-by-step guide.